The task of inductive learning from examples places constraints on the representation of training instances and concepts. These constraints are different from, and often incompatible with, the constraints placed on the representation by the performance task. This incompatibility explains why previous researchers have found it so difficult to construct good representations for inductive learning-they were trying to achieve a compromise between these two sets of constraints. To address this problem, we have developed a learning system that employs two different representations: one for learning and one for performance. The learning system accepts training instances in the "performance representation," converts them into a "learning representation" where they are inductively generalized, and then maps the learned concept back into the "performance representation." The advantages of this approach are (a) many fewer training instances are required to learn the concept, (b) the biases of the learning program are very simple, and (c) the learning system requires virtually no "vocabulary engineering" to learn concepts in a new domain.

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